R/predict.alspath.R

In SALES: The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric Least Squares (SALES) and Coupled Sparse Asymmetric Least Squares (COSALES) using Coordinate Descent and Proximal Gradient Algorithms

#' @export
predict.alspath <- function(object, newx, s = NULL, type = c("response"), ...) {
  type <- match.arg(type)
  b0 <- t(as.matrix(object$b0))
  rownames(b0) <- "(Intercept)"
  nbeta <- rbind2(b0, object$beta)
  if (!is.null(s)) {
    vnames <- dimnames(nbeta)[[1]]
    dimnames(nbeta) <- list(NULL, NULL)
    lambda <- object$lambda
    lamlist <- lambda.interp(lambda, s)
    nbeta <- nbeta[,lamlist$left,drop=FALSE]%*%Diagonal(x=lamlist$frac) +
      nbeta[,lamlist$right,drop=FALSE]%*%Diagonal(x=1-lamlist$frac)
    dimnames(nbeta) <- list(vnames, paste(seq(along = s)))
  }
  nfit <- as.matrix(as.matrix(cbind2(1, newx)) %*% nbeta)
  nfit
}